Refer
to the exploratory analysis of both the data set as mentioned in table 1 and
table 2, it can be said that three missing variables are there in case of bank_train.csv
data set [contact, month and poutcome], whereas the data set bank_test.csv also
contained three missing variables [contact, month and poutcome]. This indicates
that there are no unusual patterns evidenced in both the data set. However,
it is noted from the below mentioned histograms that there is a potential
relationship between predictor variables and target variable outcome. Mainly,
age, balance, duration and pdays have the major influence on target outcome
variable.

Answer to question [c]

Decision
tree analysis

Answer to question [d]

Logistic
regression

In
order to conduct the logistic regression model “y” is considered as the
predictor variable and age, marital, education, default, balance, housing,
loan, duration, campaign and pdays are considered as the target variable. With
the use of rapid miner tool, at first the below mentioned model has been
established. Then the logistic regression analysis has been performed.

Task
2:

The process of datafication is rapidly increased
especially in the environment of corporate sector. Big data and associated
phenomena is the current trend for keeping the information a server and execute
it in terms of making decision for further success (Zwitter, 2014). One of the
most effective phenomena of the datafication for business or individual is
social media that suppressed the capacity for changing average consumer in
terms of understanding benefits of the company (Krasnow Waterman and Bruening,
2014).

This particular study provides the information that
relates with the business ethics that has to be perceived. However, in this
particular topic analyst explain how the big data analytic tools has effective
impact on the decision making process for the company and its impact on the
ethical conceptions.

Traditional
Ethics

According to Hendler (2014), from the point of view of
moral responsibility of the business as well as individual, utilitarian and
deontological tradition has places their strong emphasize. Therefore, for the
individual, these moral agency has the much stem that regularly followed the
assumptions for the busies that helps in making decision (Krasnow Waterman and
Bruening, 2014). However, Hill (2014) argued that as the world goes changes and
modern technologies implement in the business process, all the assumption
changes and faced several challenges especially the big data. The moral agency
determinate that the degree of the entity process is the key responsible
objects of that entity (Thimmarayappa and Voges 2014). In combination with the
intrinsic factors and extraneous, moral responsibility escape the entity as
well as it defines the culpability. Following are the several entity that
innate the conditions such as –

Knowledge: The agent of the big data analytic tools is
able blame for the result of the actions that relates with the business process
as well as analytics (Hoskins, 2014). Apart from that, agent can be blamed for
the knowledge of the consequence regarding the actions that already taken for
big data analyses in terms of making decision.

Causality: Bholat (2015) argued
that agent is able to hold the responsibilities for the ethical events that
relevant to the business results especially the outcomes of the actions that
taken for decision making and future improvement in big data analytics
environment.

Choice: If the business has
liberty for choosing the alternative without any harm in the business process,
agent is able to blame for the result (Schneider, 2011).

Apart from that, the agents are also able to observe
the tend that exculpate the process full moral agency. This process is executed
if the above mentioned three different options does not execute (Krasnow
Waterman and Bruening, 2014). There are several considerations those morally
relevant outcomes of the business organization from the point of view of big
data analytics (Kirkpatrick, 2013). The sense of negative consequence is also
helps in establishing the moral obligations.

Ethical
big data challenges

Big data has several qualities such as it helps in
collecting several options that determinate the time taking for decision
making, impose the new rules and regulations and create the innovation as well
as knowledge through developing the bridge with the new datasheets and old
datasheets (Panchadsaram, 2014). Apart from that, big data allows the business
organization as well as individual in making successful decision through
analyzing the entire activities (Krasnow Waterman and Bruening, 2014). All the business
organizations have to implement the rules and regulations of the data analytics
to carry forward their business in an ethical manner. All the given rules and regulations assists
the organization to implement the tool of business analytics in an effective
manner.

However, Big data could be big (2011) argued that
ethical challenges regarding big data analytic tools increased day to day such
as global warming effect on the emissions of the many business organizations
and individuals. Citraro (2013) opined that big data analytics is the possible
effect of the individuals as well as sensory data for business. Moreover,
Correction (2013) acknowledged that in terms of measuring the real data
throughout the world big data creates lots of complexity and digitalized image
for the business. Measurement of the digital image creation is specially calls
the datafication (Gaff, Sussman and Geetter, 2014). It has been simply
identified that absence of the knowledge affect on the data regarding data
generators such as cell phone owning people, online consumers (Leonard, 2013).
The contribution of internet of things contribute lots of methods in terms of
maintaining appropriate business environment between the actor of knowledge and
actor source of power and information.

Couldry and Powell (2014) examined that leads of
global data imbalance between the different stakeholders that mostly benefitted
the corporate agencies in terms of generating knowledge of information and
intelligence. Moreover, Dhar (2014) presumed that big data correlations
suggested several causations that might be effect the ethical behavior
especially when the organization implement the true Delphian oracle within
their business process for decision making and keeping the business information
in central server. It is underlying the vulnerability that having believes.

Privacy

The life in these days becomes mirror and majority of
people become the aspects of cyber reality and recorded. Majority of the
organization becomes completely transparent for the actors for their right
access and skills (Lesk, 2013). In order to control the privacy in
datafication, the Guardian developed the RIOT (Rapid Information Overlay
Technology) that allows to track or access the information from the social
network site including IP address (Gaff, Sussman and Geetter, 2014). It can
allow to access information of one person to complete their everyday duty.

Group
Privacy

According to Dumbill (2013), majority of data analysts
use big data in terms of accessing information in terms of finding out the
shopping preferences, sleep cycles, online consumptions, health status, etc.
Moreover, there are some data analysts who used big data in terms of accessing
information that are most intelligence from the point of view of individualization
and de-individualization. This is the form of one aspect of anonymization
(Lyon, 2014). However, regarding the group privacy, issues are already
identified. The sense of de-individualization is always become the more
transparent from the point of view of statistical database. However, as the
statistical data are increasing rapidly, big data is used to analyze in a
shortest and easiest way. Hand (2011) cited that big data is used in a certain
way that consume the information about the behavior of the people. Therefore,
in the different way, it can be employed for encouraging as well as
discouraging the certain behavior of the people. Moreover, through using big
data, data analyst is able to discover the hidden correlations and increase the
effectiveness of data (Neff, 2013). Big data has the ability for creating
incentives in terms of minimizing the less transparent information within the
database. Hayes (2011) utilized that hyper connectivity of the big data is also
allowed the data analysts in implementing other strategies such as bots in the
infiltrate twitter.

Ethics

In terms of applying the big data analytics tools,
organisation has to maintain several information including standards and codes
that lay behind the development of data analysis method with proper analysis.
Majorly it has been identified that ethics of research concern the privacy
questions regarding use of social media such as Facebook, Google Plus, Twitter,
etc that are the usual suspect from the point of view of privacy. As argued by
Dumbill (2013), Facebook is the key usual suspect that comes first regarding
the question of privacy in datafication. Therefore, data analytics tools
especially the big data analytics tool has lot of reveal that much about the
very specific group in the geographical relations. On the other hand, Eckhoff
and Sommer (2014) opined that individual information is not working properly in
terms of investigating the purpose of intelligence. For the individual tag,
valuable information for the companies does not required. The main problem that
raised regarding this information is group privacy in big data analytics.
Majority of organization does not maintain or follow the ethical rules as well
as codes in terms of mitigating the non-privacy related ethical effect such as
preserving of integrity, BD&S own statement, privacy of subject
participation, etc. Inform consent is the best ethics that despite the data
from becoming published. Gaff, Sussman and Geetter (2014) argued that in terms
of analyzing and representing the pertinent social phenomena, data analysts
collected information from the social media such as Twitter, LinkedIn,
Facebook, etc without considering the lack of informed consent. According to
the constitution, this the major breach for research ethics.

Legislation

Various legislations are required for Big Data
analytics in case of every business organizations. All the business
organizations are required to comply with the rules and regulations in terms of
big data analytics (Gosain and Chugh, 2014).
The arts and humanities research council has laid down several rules and
regulations in terms of legislation requirements and amendment structure of the
organization. The various other
legislations include Privacy Act, 1988, Crimes Act, 1914, Archives Act 1983,
Evidence Act 1995 and Public Governance Act and Accountable act, 2012.

Task 3

1. Best Performing Countries

It
can be seen that Nigeria is the top performing nation among the top 10 list
with 127246092 mobile phone adoption in 2013. Nigeria is followed by Ghana,
Mali, Cote d’Lvoire, Senegal, Niger, Mauritania, Liberia, Gambia and
Guinea-Bissau.

Nations (top 10)

Adoption of Mobile Phones (2013)

Nigeria

127246092

Ghana

28026482

Mali

19749371

Cote
d’Lvoire

19390902

Senegal

13133772

Niger

7006300

Mauritania

4025478

Liberia

2550775

Gambia

1848854

Guinea-Bissau

1262700

2. Worst Performing Countries

Ghana
is the worst performing nation in adopting internet technology. Around 3502202
people does not use internet. Ghana is followed by Cote d’Lvoire, Mali, Congo
Repulic, Gambia, Liberia, Guinea, Sierra Leone, Gunia-Bissau and South Sudan.

Nations (worst 10)

Adoption of Internet (2013)

Ghana

3502202

Cote
d’Lvoire

528218

Mali

351938

Congo
Repulic

293544

Gambia

258900

Liberia

197528

Guinea

187923

Sierra
Leone

103565

Gunia-Bissau

52832

South
Sudan

0

3. Best Performing countries in terms
of adoption of landlines technology

Cabo
Verde is the best performing nation in comparison to other nine nations. Since
2000, Cabo Verde is topping the list in Africa. 0.1326 per head population is
adopting the landlines technology. Cabo Verde is followed by Gambia, Senegal,
Mauritania, Cote d’Lvoire, Ghana, Mali,
Niger, Guinea-Bissau and Nigeria.

Nations (best 10)

Adoption of Landline (2013)

Cabo
Verde

0.1326

Gambia

0.0347

Senegal

0.0243

Mauritania

0.0139

Cote
d’Lvoire

0.0134

Ghana

0.0104

Mali

0.0083

Niger

0.0056

Guinea-Bissau

0.0029

Nigeria

0.0021

4. Summary of Key Technology Adoption

The
adopters of technology have significantly increased from 2000 to 2013. The
adopters of mobiles were 42.97, internet was 7.878 and landlines were 1.859 in
2013.

Year

Mobile/Population

Internet/Population

Landlines/Population

2000

1.36

0.449

1.657

2001

2.16

0.619

1.762

2002

3.01

0.870

1.831

2003

4.03

1.112

1.906

2004

5.63

1.626

1.945

2005

8.29

1.873

1.968

2006

11.58

2.292

1.966

2007

15.88

2.803

2.065

2008

21.01

3.478

2.081

2009

25.52

3.734

2.169

2010

30.82

5.366

2.126

2011

35.24

6.315

2.135

2012

39.02

6.943

1.949

2013

42.97

7.878

1.859

3. Rationale for Graphic Design and
Functionality

1.Best
Performing Countries

For
this section, the Y-line has been set as mobile and X-line has been set has
year. For each nation, the data regarding adoption of mobiles from year 2000 to
2013 has been mentioned. Therefore, it helps in knowing in which year the
population of particular nation has adopted mobile technology which is pretty
clear that Nigeria has topped the list by huge margin. In the graph, top
section represents 10 countries of Africa for assessing the nations with most
adoption of mobile technology.

2. Worst Performing Countries

In
this part, y-line represents adoption of internet by population and top side
represents the top ten worst performing nations in adopting the technology. On
the other hand, x-axis has been taken as year representation. The nations have
been segmented on the basis of year from 2000-2013 for analyzing the adoption
of internet technology. Thus, it can be acknowledged from the graph that Ghana
is in top whereas South Sudan is at below. Apart from that, value of adoption
of technology is easily known for each year so that better utilization can be
made such as in decision making or making strategy to increase the adoption of
technology among the population.

3. Best Performing countries in terms
of adoption of landlines technology

In
this section, Y-axis represents adoption of landline by the population whereas
X-axis shows years from 2000-2013. Again, top axis represents nations of Africa
which are ten best performing nation in using the landline technology by per
head of population. Thus, the design helps in showing the data of each nation
from 2000 to 2013 in adopting technology. For every nation of Africa, data of
region, country name and year I represented which would provide great
assistance to the users of data and using it for making decision and making
further analysis. It can be make out from the graphic design of this section is
that Cabo Verde is topping the list in all consecutive years from 2000 to 2013.
Apart from that, use of technology in Niger has significantly gone down. Thus,
it can be mentioned that graphic design clearly depicts that increase and
decline in the adoption of technology can be effectively known and useful
analysis can be drawn for gaining better results.

4. Summary of Key Technology Adoption

The
graphic design of this section is divided into three technologies such as
mobile, internet and landlines. These technologies have been drawn in context
to population of regions/nations. The details of all technologies are easily
represented for each year that is from 2000 to 2013. Thus, it helped in knowing
how much the value have increased or decreased from year to year. It is
understood that adoption of mobile technology is higher in comparison to other
technology that is internet and landlines by the population of African’s
nation. Therefore, it can be drawn that designing an effective graphic design
always showcase a better platform for making effective analysis.

Sahd, C., and Thinyane, H. (2012). Community Telephone
Networks in Africa-Bridging the Gap Between Poverty and Technology.International Journal on Advances
in Networks and Services,5(1
and 2), 129-138.

Shambare, R. (2013). Factors influencing the adoption of cell
phone banking by South African students.African
Journal of Business Management,7(1),
30.

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